library(tidyverse)
library(plotly)
library(shiny)
library(flexdashboard)
library(viridis)
## Loading required package: viridisLite
data_clean <- read.csv("data_final.csv")

Project Name: Examining Demographic Patterns in NYC Shooting Data

Project Members: Chenhui Yan (CY2772), Zhaokun Lin (ZL3544), Mingyin Wang (AW3693), Zebang Zhang (ZZ3309)

Topic

In this exploratory study, we aim to analyze the geographic and temporal distribution of shootings in New York City, with an emphasis on how socioeconomic factors influence these patterns.

Motivation

Understanding the dynamics of shooting incidents in New York City is crucial for enhancing public safety and fostering resilient communities. By examining demographic patterns in shooting crime data alongside datasets on high school graduation rates and poverty levels, our project aims to identify the socioeconomic factors that influence gun violence. Analyzing how disparities in education and income relate to shooting incidents provides valuable insights for targeted interventions.

Introduction

Gun violence is a significant public health and social issue New York City, where diverse communities and complex socioeconomic conditions create a unique landscape for understanding its contributing factors. This project examines the geographic, temporal, and socioeconomic patterns of shootings in NYC to identify critical influences on gun violence. We explore how shooting incidents are distributed across boroughs and neighborhoods and examine temporal trends, such as seasonal, monthly, and time-of-day variations. Additionally, we investigate the relationship between education and gun violence, focusing on whether lower high school graduation rates correlate with higher shooting prevalence. Finally, we analyze the connection between poverty and gun violence to determine if neighborhoods with higher poverty levels experience increased incidents of shootings.

Significance

This research will aid in informing targeted public health and safety interventions by identifying the communities most impacted by shootings. Understanding these patterns will enable policymakers to implement more effective violence prevention measures and allocate resources where they are needed the most.

Research Questions

Our project focuses on examining the socioeconomic and temporal factors that influence gun violence in New York City. Specifically, we aim to answer the following questions:

Geographic and Temporal Patterns of Shootings in NYC: - How are shooting incidents distributed geographically across different boroughs and neighborhoods in New York City? - What are the temporal patterns of shootings (e.g., month, season, time of day) in different areas of NYC?

Education and Gun Violence: - Is there an association between high school graduation rates and the prevalence of shootings across NYC neighborhoods? - Are neighborhoods with lower high school graduation rates more likely to experience higher rates of gun violence?

Poverty and Gun Violence: - How does the percentage of people living below the poverty line relate to shooting incidents across neighborhoods in NYC? - Are higher poverty levels associated with an increased frequency of shootings?

Data Sources and Cleaning

In this section, describe the data sources, cleaning process, and the methods used to explore the research questions.

Neighborhood Poverty

neighborhood_poverty <- read.csv("./data/neighborhood_poverty.csv")

# Select specific columns from neighborhood_poverty
neighborhood_poverty_selected <- neighborhood_poverty %>% 
  filter(TimePeriod == '2017-21') %>% 
  filter(GeoType == 'NTA2020' ) %>% 
  select(Number, Percent, Geography)
  • Downloaded the full table from the NYC Environment and Health Data Portal
  • Filtered to include only percent data from 2017-2021 for each NTA
  • Describes estimated percentage of people whose annual income falls below 100% of the federal poverty level in each NTA

Graduated High School

neighborhood_education=read.csv("./data/graduated_high_school.csv")
neighborhood_education_selected <- neighborhood_education %>% 
  filter(TimePeriod == '2017-21') %>% 
  filter(GeoType == 'NTA2020' ) %>% 
  select(Number, Percent, Geography)
  • Downloaded the full table from the NYC Environment and Health Data Portal
  • Filtered the data to include only the percentage information for high school graduation between 2017 and 2021, focusing on Neighborhood Tabulation Areas (NTAs) as defined by the 2020 census.
  • This dataset describes the estimated percentage of individuals aged 25 and older who completed high school or obtained a high school equivalency in each NTA

Exploratory data analysis

Maps

Statistical analysis

Method for Calculating Correlation Coefficients

To assess potential linear relationships between socioeconomic variables and shooting rates, correlation coefficients were calculated. Specifically, the relationship between poverty rates and shooting rates, as well as between the percentage of high school graduates and shooting rates, was explored across different neighborhoods in NYC.

Poverty

Calculate the correlation between the poverty percentage and the incident rate.

correlation <- cor(data_clean$incident_rate_by_year_nta, data_clean$Percent_poverty, use = "complete.obs")
print(paste("Correlation coefficient: ", correlation))
## [1] "Correlation coefficient:  0.508408410575774"
data_clean %>%
  plot_ly(x = ~Percent_poverty, y = ~incident_rate_by_year_nta, 
          color = ~NTA, colors = "viridis", 
          type = "scatter", mode = "markers",
          text = ~paste("Neighborhood: ", NTA, "<br>Borough: ", BORO, 
                        "<br>% Below Poverty Line: ", Percent_poverty, 
                        "<br>Incident Rate: ", incident_rate_by_year_nta)) %>%
  layout(title = "Percent Below the Poverty Line and Incident Rate in NYC",
         xaxis = list(title = 'Percentage of People Whose Income is Below the Poverty Line'),
         yaxis = list(title = 'Incident Rate'),
         legend = list(title = list(text = 'Neighborhood')))
## Warning: Ignoring 95 observations
# Scatter plot for Brooklyn
data_clean |> 
  filter(neighbourhood_group == "Brooklyn") |> 
  plot_ly(data = _, x = ~Percent_poverty, y = ~incident_rate_by_year_nta, 
          color = ~NTA,
          colors = "plasma", 
          type = "scatter",
          mode = "markers",
          text = ~paste("Neighborhood: ", NTA, "<br>Borough: ", neighbourhood_group, 
                        "<br>% Below Poverty Line: ", Percent_poverty, 
                        "<br>Incident Rate: ", incident_rate_by_year_nta)) |> 
    layout(title = "Percent Below the Poverty Line and Incident Rate in Brooklyn",
           xaxis = list(title = 'Percentage of People Whose Income is Below the Poverty Line'),
           yaxis = list(title = 'Incident Rate'),
           legend = list(title = list(text = 'Neighborhood')))
## Warning: Ignoring 13 observations
# Scatter plot for Staten Island
data_clean |> 
  filter(neighbourhood_group == "Staten Island") |> 
  plot_ly(data = _, x = ~Percent_poverty, y = ~incident_rate_by_year_nta, 
          color = ~NTA,
          colors = "inferno", 
          type = "scatter",
          mode = "markers",
          text = ~paste("Neighborhood: ", NTA, "<br>Borough: ", neighbourhood_group, 
                        "<br>% Below Poverty Line: ", Percent_poverty, 
                        "<br>Incident Rate: ", incident_rate_by_year_nta)) |> 
    layout(title = "Percent Below the Poverty Line and Incident Rate in Staten Island",
           xaxis = list(title = 'Percentage of People Whose Income is Below the Poverty Line'),
           yaxis = list(title = 'Incident Rate'),
           legend = list(title = list(text = 'Neighborhood')))
## Warning: Ignoring 5 observations
  • Across all neighborhoods in NYC, there is a moderate positive linear relationship between the percentage of people below the poverty line (Percent_poverty) and the incident rate by neighborhood (incident_rate_by_year_nta).(r = 0.508).
  • Associations differ by borough.
  • Notably, Brooklyn (0.5686) and Staten Island (0.5576) show the highest correlations, suggesting that the relationship between poverty and incident rate is stronger in these boroughs.

Education

Calculate the correlation between the graduated in highschool percentage and the incident rate

# Calculate the correlation between the graduated in highschool percentage and the incident rate
correlation <- cor(data_clean$incident_rate_by_year_nta, data_clean$Percent_education, use = "complete.obs")
print(paste("Correlation coefficient: ", correlation))
## [1] "Correlation coefficient:  -0.274782643621427"
# Create a scatter plot to visualize the relationship
data_clean %>%
  plot_ly(x = ~Percent_education, y = ~incident_rate_by_year_nta, 
          color = ~NTA, colors = "viridis", 
          type = "scatter", mode = "markers",
          text = ~paste("Neighborhood: ", NTA, "<br>Borough: ", BORO, 
                        "<br>% graduated HS: ", Percent_education, 
                        "<br>Incident Rate: ", incident_rate_by_year_nta)) %>%
  layout(title = "Percent graduated high school and Incident Rate in NYC",
         xaxis = list(title = 'Percentage of People graduated in high school'),
         yaxis = list(title = 'Incident Rate'),
         legend = list(title = list(text = 'Neighborhood')))
## Warning: Ignoring 95 observations
# Scatter plot for The Bronx
data_clean |> 
  filter(neighbourhood_group == "Bronx") |>
  plot_ly(data = _, x = ~Percent_poverty, y = ~incident_rate_by_year_nta, 
          color = ~NTA,
          colors = "magma", 
          type = "scatter",
          mode = "markers",
          text = ~paste("Neighborhood: ", NTA, "<br>Borough: ", neighbourhood_group, 
                        "<br>% graduated HS: ", Percent_education, 
                        "<br>Incident Rate: ", incident_rate_by_year_nta)) |> 
   layout(title = "Percent graduated high school and Incident Rate in The Bronx",
           xaxis = list(title = 'Percentage of People graduated in high school'),
           yaxis = list(title = 'Incident Rate'),
           legend = list(title = list(text = 'Neighborhood')))
## Warning: Ignoring 25 observations
# Scatter plot for Staten Island
data_clean |> 
  filter(neighbourhood_group == "Staten Island") |>
  plot_ly(data = _, x = ~Percent_poverty, y = ~incident_rate_by_year_nta, 
          color = ~NTA,
          colors = "inferno", 
          type = "scatter",
          mode = "markers",
          text = ~paste("Neighborhood: ", NTA, "<br>Borough: ", neighbourhood_group, 
                        "<br>% graduated HS: ", Percent_education, 
                        "<br>Incident Rate: ", incident_rate_by_year_nta)) |> 
   layout(title = "Percent graduated high school and Incident Rate in Staten Island",
           xaxis = list(title = 'Percentage of People graduated in Staten Island'),
           yaxis = list(title = 'Incident Rate'),
           legend = list(title = list(text = 'Neighborhood')))
## Warning: Ignoring 5 observations

The correlation coefficient is -0.2748, indicating a negative relationship between the percentage of people who graduated from high school and the incident rate. This means that there is a tendency for higher education levels to be associated with lower incident rates * Across all neighborhoods in NYC,there is no clear linear trend between high school graduation rates and incident rates in Manhattan. * Associations differ by borough. * Notably, The Bronx (-0.3996) and Staten Island (-0.6452) show the highest correlations, suggesting that the relationship between percentage of people who graduated from high school and incident rate is stronger in these boroughs.

Discussion

Limitations